Multiscale complexity of functional dynamics in visual cortex – Complex-V1

One of the characteristics of neural activity in neocortical networks is that there is a considerable level of self-sustained ongoing activity, which exhibits highly complex but structured spatiotemporal patterns of action potentials and whose irregularity in time is often interpreted as "noise". In view of the recurrent nature of the network, where most links between neural units are achieved through distributed reverberating loops, it is therefore impossible to apply the classic paradigm of distinguishing the "signal" from the "noise".

In this project, we would like to depart from this paradigm and rather consider that information is potentially present in ongoing activity (for instance as internally stored memories) and that external inputs, carrying stimulus-driven information, are interacting non-linearly with the ongoing activity. We aim at characterizing network states using diverse methods applied at different scales of spatial integration, from the microscopic (conductance and single-neuron level), up to large populations of neurons measured mesoscopically (multiple recordings and voltage sensitive dye imaging). We will provide a characterization of the correlation state of network activity, as seen through the measurement techniques associated to each scale:

I. At the single-neuron level, intracellular recordings will be used to resolve subthreshold membrane potential fluctuations, of synaptic origin, and analyze a dynamic multiscale "image" of the activity of the effective afferent network in which the cell is embedded at any point in time.

II. At the level of populations, the recording of many neurons simultaneously will be confronted with theories inspired from Ising models and provide a characterization of the network state through pairwise correlations.

III. At more macroscopic levels, we will use local field potential recordings using array of electrodes, and voltage-sensitive dye imaging to record simultaneously larger assemblies and extract possible relations between correlation patterns and the context of functional cortical maps.

By using theoretical approaches adapted to such scales, such as electrodynamics or mean-field models, we also hope to provide characterizations of network states during ongoing activity, and during visual inputs of different dimensionalities. In all cases, the in vivo experiments and modeling will be done in parallel with in vitro experiments to determine key aspects and correlation measures in controlled conditions, as well as to validate or test some of the assumptions of the models. These different studies should allow us to study the interdependency between different levels of neural-based processing in neocortical networks, and address experimentally the concepts of “emergence” (micro to macro) and “immergence” (macro to micro) characteristic of complex dynamic systems

By this interdisciplinary approach, we hope to provide decisive data, tools and concepts on the different network states involved in visual processing, with possible future applications as diverse as artificial vision, "mind reading" in brain imaging, brain and machine interface in the field of Neuroprosthetics and Medicine, and life-inspired computing architectures in the field of Information and Technology.